Personalize treatment for longitudinal data using unspecified random-effects model

We develop new modeling for personalized treatment for longitudinal studies involving high heterogeneity of treatment effects. Incorporating subject-specific information into the treatment assignment is crucial since different individuals can react to the same treatment very differently. We estimate unobserved subject-specific treatment effects through conditional random-effects modeling, and apply the random forest algorithm to allocate effective treatments for individuals. The advantage of our approach is that random-effects estimation does not rely on the normality assumption. In theory, we show that the proposed randomeffect estimator is consistent and more efficient than the random-effect estimator that ignores correlation information from longitudinal data. Simulation studies and a data example from an HIV clinical trial also confirm that the proposed method can efficiently identify the best treatments for individual patients.